Integer Optimization Model and Algorithm for the Stem Cell Culturing Problem

Omega ◽  
2021 ◽  
pp. 102566
Author(s):  
Jongyoon Park ◽  
Jinil Han ◽  
Kyungsik Lee
Keyword(s):  
Stem Cell ◽  
Optimization Model ◽  
Integer Optimization ◽  
Cell Culturing

scholarly journals Morphological and immunocytochemical characteristics indicate the yield of early progenitors and represent a quality control for human mesenchymal stem cell culturing

2009 ◽  
Vol 214 (5) ◽  
pp. 759-767 ◽  
Author(s):  
Florian Haasters ◽  
Wolf Christian Prall ◽  
David Anz ◽  
Carole Bourquin ◽  
Christoph Pautke ◽  
...  
Keyword(s):  
Quality Control ◽  
Stem Cell ◽  
Mesenchymal Stem Cell ◽  
Human Mesenchymal Stem Cell ◽  
Cell Culturing

scholarly journals Growing, Tracking, and Directing Bone Marrow Derived Stem Cells From Two-Dimensional and Three-Dimensional Cell Culture Microenvironments

2019 ◽  
Author(s):  
Tiffany Miller
Keyword(s):  
Stem Cells ◽  
Cell Culture ◽  
Bone Marrow ◽  
Stem Cell ◽  
Three Dimensional ◽  
Inflammatory Biomarkers ◽  
Two Dimensional ◽  
Cell Culturing ◽  
Dimensional Cell ◽  
Stimulation Of

<p>Bone marrow derived stem cells express biomarkers capable of facilitating adhesion to the cell culturing microenvironment, thereby, influencing their proliferation, migration, and differentiation. In particular, biological biomarkers of mesenchymal stem cells include, but are not limited to, CD14-, CD19-, CD34-, CD45-, CD29, CD44, CD73+, CD90+, CD105+, CD106, CD166, Stro-1, and HLADR. The relationship between the stem cell biology and the materials and methods forming a cell culturing microenvironment serves as a critical aspect in the successful adhesion and growth within two-dimensional cell culture microenvironments such as polystyrene, laminin, fibronectin, or poly-L-lysine and within three-dimensional cell culture microenvironments such as hydrogel, ceramic, collagen, polymer based nanofibers, agitation, forced floating, and hang drop systems. Further, electrical stimulation of the stem cells may be implemented during the cell culturing process to measure stem cell growth and to determine stem cell viability. In addition, electrical stimulation of implanted stem cells may facilitate tracking by measuring stem cell migration distance and travel area. Although many biochemical and inflammatory biomarkers are expressed based on severity in stroke including, but not limited to, Interluken-6 (IL-6), tumor necrosis factor-alpha (TNF-α), and glutamate (Glu), current methodologies of stem cell directing lack localization and biological effector specificity. Biological effector bound magnetic particle stem cells may serve as a potential treatment method in ischemic stroke. In particular, a stem cell biomarker may be configured to communicate with inflammatory biomarkers, thus, more efficiently delivering the stem cells to site specific areas having the most severely affected <i>in-vivo</i> biochemical microenvironments.</p>

scholarly journals Advances in Human Stem Cell-Derived Neuronal Cell Culturing and Analysis

2019 ◽  
pp. 299-329 ◽  
Author(s):  
Laura Ylä-Outinen ◽  
Jarno M. A. Tanskanen ◽  
Fikret E. Kapucu ◽  
Anu Hyysalo ◽  
Jari A. K. Hyttinen ◽  
...  
Keyword(s):  
Stem Cell ◽  
Neuronal Cell ◽  
Human Stem Cell ◽  
Cell Culturing

Abstract 3486: Novel cell models of ovarian cancer for target discovery and validation obtained through stem cell culturing technology

2012 ◽  
Author(s):  
Peter F. Young ◽  
Zhuangyu Pan ◽  
Penny Roberts ◽  
Jeff Hooley ◽  
Doug Smith ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Stem Cell ◽  
Cell Models ◽  
Target Discovery ◽  
Cell Culturing

High-speed rail cost recovery time based on an integer optimization model

2014 ◽  
Vol 49 (5) ◽  
pp. 634-647 ◽  
Author(s):  
Shoupeng Tang ◽  
Stephen D. Boyles ◽  
Nan Jiang
Keyword(s):  
Optimization Model ◽  
Recovery Time ◽  
High Speed ◽  
Cost Recovery ◽  
Integer Optimization ◽  
High Speed Rail

scholarly journals Detecting Customer Trends for Optimal Promotion Targeting

2020 ◽  
Author(s):  
Lennart Baardman ◽  
Setareh Borjian Boroujeni ◽  
Tamar Cohen-Hillel ◽  
Kiran Panchamgam ◽  
Georgia Perakis
Keyword(s):  
Optimization Model ◽  
Problem Definition ◽  
Customer Relationships ◽  
Mixed Integer ◽  
Percentage Error ◽  
Integer Optimization ◽  
Demand Model ◽  
Support Tool ◽  
Demand Models ◽  
Transactional Data

Problem definition: Retailers have become increasingly interested in personalizing their products and services such as promotions. For this, we need new personalized demand models. Unfortunately, social data are not available to many retailers because of cost and privacy issues. Thus, we focus on the problem of detecting customer relationships from transactional data and using them to target promotions to the right customers. Academic/practical relevance: From an academic point of view, this paper solves the novel problem of jointly detecting customer trends and using them for optimal promotion targeting. Notably, we estimate the causal customer-to-customer trend effect solely from transactional data and target promotions for multiple items and time periods. In practice, we provide a new tool for Oracle Retail clients that personalizes promotions. Methodology: We develop a novel customer trend demand model distinguishing between a base purchase probability, capturing factors such as price and seasonality, and a customer trend probability, capturing customer-to-customer trend effects. The estimation procedure is based on regularized bounded variables least squares and instrumental variable methods. The resulting customer trend estimates feed into the dynamic promotion targeting optimization problem, formulated as a nonlinear mixed-integer optimization model. Though it is nondeterministic polynomial-time hard, we propose a greedy algorithm. Results: We prove that our customer-to-customer trend estimates are statistically consistent and that the greedy optimization algorithm is provably good. Having access to Oracle Retail fashion client data, we show that our demand model reduces the weighted-mean absolute percentage error by 11% on average. Also, we provide evidence of the causality of our estimates. Finally, we demonstrate that the optimal policy increases profits by 3%–11%. Managerial implications: The demand model with customer trend and the optimization model for targeted promotions form a decision-support tool for promotion planning. Next to general planning, it also helps to find important customers and target them to generate additional sales.

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